R Notebook

Variables:

Risk
Money
Security
Good time Help Success Proper Environment Tradition Creativity

Friends important Family important Leisure time Happiness Health (subjective) Satisfaction Freedom

Sex Age Country Wave Marital status Children Employment Education

library(data.table)
library(tidyr)

#read the data (Wave 5)

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
head(WV5_data_df[, 1:5])

clean the data set

library(dplyr)

#rename the variables
WV5_data <- WV5_data_df %>%
  rename(sex = V235, age = V237, country = V2, wave = V1, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V22, freedom = V46, marital_status = V55, children = V56, creativity = V80, money = V81, security = V82, goodtime = V83, help = V84, success = V85, risk = V86, proper = V87, environment = V88, tradition = V89, employment = V241, education = V238)
WV5_data

#select only the variables of interest
WV5_data <- WV5_data %>%
  select(sex, age, country, wave, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom, marital_status, children, creativity, money, security, goodtime, help, success, risk, proper, environment, tradition, employment, education)
WV5_data

#decode the country names 
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV5_data$country_lab = countrynames$name [match(WV5_data$country, countrynames$code)]
table(WV5_data$country_lab)

            Andorra           Argentina           Australia              Brazil            Bulgaria        Burkina Faso              Canada               Chile 
               1003                1002                1421                1500                1001                1534                2164                1000 
              China            Colombia          Cyprus (G)               Egypt            Ethiopia             Finland              France             Georgia 
               1991                3025                1050                3051                1500                1014                1001                1500 
            Germany               Ghana       Great Britain           Guatemala           Hong Kong             Hungary               India           Indonesia 
               2064                1534                1041                1000                1252                1007                2001                2015 
               Iran                Iraq               Italy               Japan              Jordan            Malaysia                Mali              Mexico 
               2667                2701                1012                1096                1200                1201                1534                1560 
            Moldova             Morocco         Netherlands         New Zealand              Norway                Peru              Poland             Romania 
               1046                1200                1050                 954                1025                1500                1000                1776 
             Russia              Rwanda            Slovenia        South Africa         South Korea               Spain              Sweden         Switzerland 
               2033                1507                1037                2988                1200                1200                1003                1241 
             Taiwan            Thailand Trinidad and Tobago              Turkey             Ukraine       United States             Uruguay            Viet Nam 
               1227                1534                1002                1346                1000                1249                1000                1495 
             Zambia 
               1500 

WV5_data

NA
NA

#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)

` ``{r} #rename variables

WV6_data <- WV6_data %>%
  rename(wave = V1, sex = V240, age = V242,country = V2, marital_status = V57, children = V58, employment = V229, education = V248, risk = V76, money = V71, security = V72, goodtime =  V73, help = V74B, success = V75, proper = V77, environment = V78, tradition = V79, creativity = V70, family_important = V4, friends_important = V5, leisure_time = V6, happiness = V10, health = V11, satisfaction = V23, freedom = V55 )


#select only the variables of interest
WV6_data <- WV6_data %>%
  select(sex, age, country, wave, marital_status, children, employment, education, risk, money, security, goodtime, help, success, proper, environment, tradition, creativity, family_important, friends_important, leisure_time, happiness, health, satisfaction, freedom)
WV6_data

NA

#decode daraset (Wave 6)

countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country_lab = countrynames$name [match(WV6_data$country, countrynames$code)]
table(WV6_data$country_lab)

            Algeria           Argentina             Armenia           Australia          Azerbaijan             Belarus              Brazil               Chile 
               1200                1030                1100                1477                1002                1535                1486                1000 
              China            Colombia          Cyprus (G)             Ecuador               Egypt             Estonia             Georgia             Germany 
               2300                1512                1000                1202                1523                1533                1202                2046 
              Ghana               Haiti           Hong Kong               India                Iraq               Japan              Jordan          Kazakhstan 
               1552                1996                1000                4078                1200                2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon               Libya            Malaysia              Mexico             Morocco         Netherlands 
               1303                1500                1200                2131                1300                2000                1200                1902 
        New Zealand             Nigeria            Pakistan           Palestine                Peru         Philippines              Poland               Qatar 
                841                1759                1200                1000                1210                1200                 966                1060 
            Romania              Russia              Rwanda           Singapore            Slovenia        South Africa         South Korea               Spain 
               1503                2500                1527                1972                1069                3531                1200                1189 
             Sweden              Taiwan            Thailand Trinidad and Tobago             Tunisia              Turkey             Ukraine       United States 
               1206                1238                1200                 999                1205                1605                1500                2232 
            Uruguay          Uzbekistan               Yemen            Zimbabwe 
               1000                1500                1000                1500 

WV6_data

#combine the 2 dataset (Wave 6 + Wave 5)

WV5_data

WV6_data

data = rbind(WV5_data, WV6_data)
data

#number of countries

length(unique(data$country_lab))

[1] 80

#number of participants

nrow(data)

[1] 173540

#exclusion of participants

data = subset(data, risk> 0 & sex > 0 & age > 0 & education > 0 & employment > 0 & marital_status > 0 & children >= 0)
data

NA

#number of males vs females (1 = males; 2 = females)

table(data$sex)

    1     2 
71689 77937 

#create a categorical age variable

data$agecat[data$age<20]="15-19"
data$agecat[data$age>=20 & data$age <30] = "20-29"
data$agecat[data$age>=30 & data$age <40] = "30-39"
data$agecat[data$age>=40 & data$age <50] = "40-49"
data$agecat[data$age>=50 & data$age <60] = "50-59"
data$agecat[data$age>=60 & data$age <70] = "60-69"
data$agecat[data$age>=70 & data$age <80] = "70-79"
data$agecat[data$age>=80] = "80+"

#gender variables

data$sex[data$sex == 1] <- "male"
data$sex[data$sex == 2] <- "female"

#average age of participants

mean(data$age)

[1] 41.59569

#age range

range(data$age) 

[1] 15 99

#risk taking Frequency

library(ggplot2)
ggplot(data, aes(x = risk)) +
  geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
  labs(x = "Risk Taking", y = "Frequency", title = "Histogram of Risk Taking") +
  theme_minimal()

#age frequency

ggplot(data, aes(x = age)) +
  geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
  labs(x = "Age", y = "Frequency", title = "Histogram of Age Distributionn") +
  theme_minimal()

#age vs risk taking

ggplot(data, aes(x = agecat, y = risk)) +
  geom_boxplot() +
  labs(title = "Boxplot of Risk and Adventure by Age",
       x = "Age",
       y = "Risk and Adventure") +
  theme_minimal()

NA
NA

#sex vs risk taking

ggplot(data, aes(as.factor(sex), risk))+
  geom_boxplot()

summary(data)

     sex                 age          country           wave       family_important friends_important  leisure_time      happiness          health        satisfaction   
 Length:149626      Min.   :15.0   Min.   : 12.0   Min.   :5.000   Min.   :-5.000   Min.   :-5.000    Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000  
 Class :character   1st Qu.:28.0   1st Qu.:276.0   1st Qu.:5.000   1st Qu.: 1.000   1st Qu.: 1.000    1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 5.000  
 Mode  :character   Median :39.0   Median :484.0   Median :6.000   Median : 1.000   Median : 2.000    Median : 2.000   Median : 2.000   Median : 2.000   Median : 7.000  
                    Mean   :41.6   Mean   :481.5   Mean   :5.552   Mean   : 1.094   Mean   : 1.661    Mean   : 1.871   Mean   : 1.865   Mean   : 2.106   Mean   : 6.755  
                    3rd Qu.:53.0   3rd Qu.:710.0   3rd Qu.:6.000   3rd Qu.: 1.000   3rd Qu.: 2.000    3rd Qu.: 2.000   3rd Qu.: 2.000   3rd Qu.: 3.000   3rd Qu.: 8.000  
                    Max.   :99.0   Max.   :894.0   Max.   :6.000   Max.   : 4.000   Max.   : 4.000    Max.   : 4.000   Max.   : 4.000   Max.   : 5.000   Max.   :10.000  
                                                                   NA's   :221      NA's   :351       NA's   :698      NA's   :573      NA's   :230      NA's   :340     
    freedom       marital_status     children       creativity         money           security         goodtime           help          success            risk      
 Min.   :-5.000   Min.   :1.000   Min.   :0.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.00   Min.   :-5.000   Min.   :1.000  
 1st Qu.: 6.000   1st Qu.:1.000   1st Qu.:0.000   1st Qu.: 2.000   1st Qu.: 3.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.00   1st Qu.: 2.000   1st Qu.:3.000  
 Median : 7.000   Median :1.000   Median :2.000   Median : 3.000   Median : 4.000   Median : 2.000   Median : 3.000   Median : 2.00   Median : 3.000   Median :4.000  
 Mean   : 7.004   Mean   :2.715   Mean   :1.843   Mean   : 2.718   Mean   : 3.846   Mean   : 2.374   Mean   : 3.273   Mean   : 2.29   Mean   : 2.951   Mean   :3.801  
 3rd Qu.: 9.000   3rd Qu.:6.000   3rd Qu.:3.000   3rd Qu.: 4.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.: 3.00   3rd Qu.: 4.000   3rd Qu.:5.000  
 Max.   :10.000   Max.   :6.000   Max.   :8.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.00   Max.   : 6.000   Max.   :6.000  
 NA's   :838                                      NA's   :972      NA's   :602      NA's   :442      NA's   :566      NA's   :44862   NA's   :703                     
     proper        environment       tradition        employment      education     country_lab           agecat         
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :1.000   Min.   :1.000   Length:149626      Length:149626     
 1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.:1.000   1st Qu.:3.000   Class :character   Class :character  
 Median : 2.000   Median : 2.000   Median : 2.000   Median :3.000   Median :5.000   Mode  :character   Mode  :character  
 Mean   : 2.533   Mean   : 2.468   Mean   : 2.511   Mean   :3.406   Mean   :5.501                                        
 3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.:5.000   3rd Qu.:7.000                                        
 Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   :8.000   Max.   :9.000                                        
 NA's   :541      NA's   :561      NA's   :518                                                                           

data = na.omit(data)
summary(data)

     sex                 age           country           wave       family_important friends_important  leisure_time      happiness          health        satisfaction   
 Length:101172      Min.   :15.00   Min.   : 12.0   Min.   :5.000   Min.   :-5.000   Min.   :-5.000    Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000  
 Class :character   1st Qu.:27.00   1st Qu.:268.0   1st Qu.:5.000   1st Qu.: 1.000   1st Qu.: 1.000    1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 1.000   1st Qu.: 5.000  
 Mode  :character   Median :39.00   Median :458.0   Median :5.000   Median : 1.000   Median : 2.000    Median : 2.000   Median : 2.000   Median : 2.000   Median : 7.000  
                    Mean   :41.11   Mean   :474.4   Mean   :5.348   Mean   : 1.099   Mean   : 1.652    Mean   : 1.893   Mean   : 1.889   Mean   : 2.098   Mean   : 6.692  
                    3rd Qu.:53.00   3rd Qu.:710.0   3rd Qu.:6.000   3rd Qu.: 1.000   3rd Qu.: 2.000    3rd Qu.: 2.000   3rd Qu.: 2.000   3rd Qu.: 3.000   3rd Qu.: 8.000  
                    Max.   :99.00   Max.   :894.0   Max.   :6.000   Max.   : 4.000   Max.   : 4.000    Max.   : 4.000   Max.   : 4.000   Max.   : 5.000   Max.   :10.000  
    freedom      marital_status     children       creativity         money           security         goodtime           help           success            risk      
 Min.   :-5.00   Min.   :1.000   Min.   :0.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :-5.000   Min.   :1.000  
 1st Qu.: 5.00   1st Qu.:1.000   1st Qu.:0.000   1st Qu.: 2.000   1st Qu.: 3.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.:3.000  
 Median : 7.00   Median :1.000   Median :2.000   Median : 2.000   Median : 4.000   Median : 2.000   Median : 3.000   Median : 2.000   Median : 3.000   Median :4.000  
 Mean   : 6.91   Mean   :2.769   Mean   :1.835   Mean   : 2.699   Mean   : 3.842   Mean   : 2.363   Mean   : 3.243   Mean   : 2.281   Mean   : 2.937   Mean   :3.827  
 3rd Qu.: 9.00   3rd Qu.:6.000   3rd Qu.:3.000   3rd Qu.: 4.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.: 3.000   3rd Qu.: 4.000   3rd Qu.:5.000  
 Max.   :10.00   Max.   :6.000   Max.   :8.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   : 6.000   Max.   :6.000  
     proper        environment       tradition       employment      education     country_lab           agecat         
 Min.   :-5.000   Min.   :-5.000   Min.   :-5.00   Min.   :1.000   Min.   :1.000   Length:101172      Length:101172     
 1st Qu.: 1.000   1st Qu.: 2.000   1st Qu.: 1.00   1st Qu.:1.000   1st Qu.:3.000   Class :character   Class :character  
 Median : 2.000   Median : 2.000   Median : 2.00   Median :3.000   Median :5.000   Mode  :character   Mode  :character  
 Mean   : 2.538   Mean   : 2.452   Mean   : 2.51   Mean   :3.467   Mean   :5.309                                        
 3rd Qu.: 3.000   3rd Qu.: 3.000   3rd Qu.: 3.00   3rd Qu.:5.000   3rd Qu.:7.000                                        
 Max.   : 6.000   Max.   : 6.000   Max.   : 6.00   Max.   :8.000   Max.   :9.000                                        

#ris vs education
ggplot(data, aes(risk, education))+
  geom_point()+
  geom_smooth(method = "lm")

model = lm(risk ~ education, data = data)
summary(model)

Call:
lm(formula = risk ~ education, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0532 -1.0532  0.1564  1.2612  2.3660 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.10560    0.01183  347.08   <2e-16 ***
education   -0.05240    0.00202  -25.95   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.589 on 101170 degrees of freedom
Multiple R-squared:  0.00661,   Adjusted R-squared:  0.0066 
F-statistic: 673.1 on 1 and 101170 DF,  p-value: < 2.2e-16

ggplot(data, aes(risk, freedom))+
  geom_point()+
  geom_smooth(method = "lm")

model1 = lm(risk ~ freedom, data = data)
summary(model1)

Call:
lm(formula = risk ~ freedom, data = data)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3968 -1.1100  0.1769  1.2247  2.3204 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.157773   0.014987  277.43   <2e-16 ***
freedom     -0.047814   0.002045  -23.38   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.59 on 101170 degrees of freedom
Multiple R-squared:  0.005375,  Adjusted R-squared:  0.005365 
F-statistic: 546.7 on 1 and 101170 DF,  p-value: < 2.2e-16

ggplot(data, aes(as.factor(wave), risk))+
  geom_boxplot()

ggplot(data, aes(risk, age))+
  geom_point()+
  geom_smooth(method = "lm")

```